Study Setting and Design
The United Nations (UN) Statistics Division has subdivided the African continent into five regions. Among these regions, East African countries make the most significant region that includes 19 countries (Burundi, Comoros, Djibouti, Ethiopia, Eritrea, Kenya, Madagascar, Malawi, Mauritius, Mozambique, Reunion, Rwanda, Seychelles, Somalia, Somaliland, Tanzania, Uganda, Zambia, and Zimbabwe). This study was a secondary data analysis based on DHS. Of these 19 East African countries, six countries (Djibouti, Somalia, Somaliland, Seychelles and Mauritius, Reunion) have no data on their history and Eretria has no recent data. In addition, six countries (Zambia, Zimbabwe, Tanzania, Uganda, Burundi, and Malawi) have no all the six components of ANC services for the most recent birth during ANC, developed by WHO on their recent DHS data. So, the final analysis was conducted from the data of six countries, including Ethiopia, Kenya, Comoros, Madagascar, Mozambique, and Rwanda. During the measure DHS survey, a community-based cross-sectional study design was used.
Data Source, Extraction and Sampling Strategy
The data of these six East Africa countries were accessed from the DHS program official database (www.measuredhs.com) after authorization was granted through an online request by explaining the goal of our study. We used the individual Record (IR file) data set and extracted the dependent and independent variables. To collect knowledge comparable across countries globally, the DHS program adopts standardized methods involving uniform questionnaires, manuals, and field procedures. DHS is a nationally representative household survey that offers data from a wide variety of population, health, and nutrition tracking and effect assessment measures with face-to-face interviews of women aged 15 to 49. Stratified, multi-stage, random sampling is used in the surveys. Detailed survey techniques and methods of sampling used to collect data have been recorded elsewhere (12).
Measurements
Quality of ANC refers to receiving all essential components of ANC services such as blood pressure measurement, blood test, urine test, informed on possible complications, counseling on nutrition, and advice on birth preparedness plan during pregnancy (13, 14). The outcome variable consists of these six questions. Each question has a binary response (1 = Yes and 0 = No). If the pregnant women are received all six essential ANC components, it is said to be quality ANC, and if not, it is said to be no quality ANC. Explanatory variables were age, residence, women's level of education, literacy, wealth index, birth order, country, husband education level, number of ANC visits, and ANC provider.
Data Processing and Analysis
Data processing and analysis were performed using STATA 15 software. The data were weighted using sampling weight, primary sampling unit, and strata before any statistical analysis to restore the survey's representativeness and tell the STATA to consider the sampling design when calculating standard errors to get reliable statistical estimates. Cross tabulations and summary statistics were conducted to describe the study population.
Since the DHS data has a hierarchical nature, women within a cluster may be like each other more than women in the other cluster. Due to this, the assumption of independence of observations and equal variance across clusters might be violated. Therefore, an advanced statistical model must consider the between cluster variability to get a reliable standard error and unbiased estimate. Furthermore, by considering the dichotomous nature of the outcome variable, multilevel mixed-effect logistic regression was fitted. Model comparison was made based on Akaike and Bayesian Information Criteria (AIC and BIC). A mixed-effect model with the lowest Information Criteria (AIC and BIC) was selected.
The individual and community level variables associated with the Quality of ANC were checked independently in the bi-variable multilevel mixed-effect logistic regression model, and variables that were statistically significant at p-value 0.20 in the bi-variable multilevel mixed-effects logistic regression analysis were considered for the final individual and community level model adjustments. In the multivariable multilevel mixed-effect analysis, variables with a p-value ≤of 0.05 were declared significant determinants of the Quality of ANC service. Intraclass correlation coefficient (ICC) was used to check whether the multilevel model is appropriate and how much of the overall variation in the response is explained by clustering.
Four models were fitted. The first was the null model that did not include exposure variables used to verify community variance and provide evidence to assess random effects at the community level. Then, model I was the multivariable model adjustment for individual-level variables, and model II was adjusted for community-level factors. In model III, the outcome variable was equipped with potential candidate variables from individual and community-level variables.
The fixed effects (a measure of association) were used to estimate the association between the Quality of ANC service and explanatory variables and expressed as an odds ratio with a 95% confidence interval. Regarding the variation (random-effects) measures, Community-level variance with standard deviation and intra-cluster correlation coefficient (ICC) was used.